Pathways to identify and reduce uncertainties in agricultural climate impact assessments

Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.

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Acknowledgements

This work supported G.J.O’L. in the AGFACE project that was funded by the Australian Grains Research and Development Corporation and the Australian Government Department of Agriculture in a collaboration between Agriculture Victoria and the University of Melbourne. The contributions of A.C.R. were made possible by support from the NASA Earth Science Division to the GISS Climate Impacts Group.

Author information

Authors and Affiliations

  1. New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia Bin Wang, Linchao Li & De Li Liu
  2. Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, New South Wales, Australia Bin Wang & De Li Liu
  3. NASA Goddard Institute for Space Studies, New York, NY, USA Jonas Jägermeyr, Alex C. Ruane & Cynthia Rosenzweig
  4. Columbia University, Climate School, New York, NY, USA Jonas Jägermeyr
  5. Potsdam Institute for Climate Impacts Research, Member of the Leibniz Association, Potsdam, Germany Jonas Jägermeyr
  6. Agriculture Victoria, Department of Energy, Environment and Climate Action, Horsham, Victoria, Australia Garry J. O’Leary
  7. Faculty of Science, The University of Melbourne, Parkville, Victoria, Australia Garry J. O’Leary
  8. Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany Daniel Wallach
  9. College of Land Science and Technology, China Agricultural University, Beijing, China Puyu Feng
  10. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, China Linchao Li & Qiang Yu
  11. Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia De Li Liu
  12. GreenCollar, The Rocks, Sydney, New South Wales, Australia Cathy Waters
  13. Technical University of Munich, School of Life Sciences, Digital Agriculture, HEF World Agricultural Systems Center, Freising, Germany Senthold Asseng
  1. Bin Wang